amoa-label-taxonomy

You can enforce exactly-one status, priority, and type labels on issues and PRs using validation scripts and lifecycle procedures. By applying cardinality rules through the GitHub CLI, you prevent common labeling errors and ensure your repository taxonomy remains consistent and machine-readable.

GitHub label cardinality rules define constraints like exactly-one status, priority, and type label, plus at-most-one assign label per issue. These rules prevent workflow regressions by ensuring automated triage and orchestration tasks always have the required metadata to process tickets correctly.

Automate triage and assignment by running GitHub CLI scripts that apply required metadata to new issues and transition their status labels. This procedure routes tasks through lifecycle stages like ai-review and human-review, ensuring orchestrated agents receive properly labeled tickets for processing.

Yes, the label taxonomy validation operates within AI Maestro orchestrator workflows. It ensures new issues receive required metadata for automated agents, enabling safe reassignment and structured task movement through ai-review, human-review, and merge-release stages without manual intervention.

You can detect and fix label taxonomy violations in bulk using provided CLI validation scripts and query patterns. These tools identify issues missing required status, priority, or type labels and execute remediation steps to restore taxonomy compliance across your repository.

You should avoid automated label enforcement if your repository lacks a strict taxonomy definition or relies on free-form tagging. The system requires predefined cardinality rules, so repositories with highly dynamic or unstructured labeling workflows may experience regressions or validation conflicts.